NSTL国家科技图书文献中心

1. Competing Dual-Network with Pseudo-Supervision Rectification for Semi-Supervised Medical Image Segmentation NSTL国家科技图书文献中心

Ping Zhou | Feng Chen ... - 《Pattern Recognition and Computer Vision,Part XIV》 - Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 545～559 - 共15页

摘要：Semi-supervised medical image segmentation | potential noise in the pseudo-supervision* process* | Dual-Network with Pseudo-Supervision* Rectification* | mechanism for single image pair and the pseudo-supervision | -supervision* stage is effectively alleviated. Experimental*

关键词： Semi-Supervised learning | Medical image segmentation | Competing dual-Network | Rectified pseudo-Supervision | Data augmentation

2. Prior-Aware Cross Pseudo Supervision for Semi-supervised Tooth Segmentation NSTL国家科技图书文献中心

Tingyi Lin | Pengju Lyu ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 169～179 - 共11页

摘要： performance. In this paper, we present a cascade semi | strategies into a cycle supervision* module. We evaluate C* | Automatic and precise tooth segmentation is | crucial in computer aided dentistry, serving a pivotal | role in various applications likes diagnosis and

关键词： Cross pseudo supervision | Cascade network | Consistency regularization | Prior constraint

3. Class-Aware Cross Pseudo Supervision Framework for Semi-Supervised Multi-organ Segmentation in Abdominal CT Scans NSTL国家科技图书文献中心

Deqian Yang | Haochen Zhao ... - 《Pattern Recognition and Computer Vision,Part XIV》 - Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 148～162 - 共15页

摘要： semi-supervised learning (SSL) techniques have been | propose a class-aware cross pseudo supervision* (C~2PS* | ) framework, which built upon cross pseudo supervision* (CPS* | Automatic multi-organ segmentation in | abdominal computed tomography (CT) scans is crucial for

关键词： Multi-organ segmentation | Abdominal CT | Class imbalance

4. Enhancing Semi-Dense Feature Matching Through Probabilistic Modeling of Cascaded Supervision and Consistency NSTL国家科技图书文献中心

Hongchang Min | Yihong Tang ... - 《Pattern Recognition and Computer Vision,Part XV》 - Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 553～566 - 共14页

摘要： propose an approach termed Cascaded Supervision | cascading supervision* of the matching results at various* | Local feature matching, which identifies | correspondences between image pairs, remains a fundamental | challenge in computer vision. Current methods usually

关键词： Feature matching | Cascade networks | Neighborhood consistency | Probabilistic modeling

5. Driver Fatigue Recognition Based on EEG Signal and Semi-supervised Learning NSTL国家科技图书文献中心

Lin Chen | Xiaobo Chen - 《Intelligence science V》 - International conference on intelligence science - 2025, - 273～285 - 共13页

摘要： Semi-supervised Label Propagation with Optimal Graph | Driving fatigue has become a serious hidden | danger to road traffic safety. Drivers in a fatigued | state often have problems such as delayed reactions | and lack of concentration, which increases the risk

关键词： Semi-supervision Learning | Graph Learning | Driver Fatigue Recognition | EEG data

6. Preprocessing of Prior Knowledge Before Semi-supervised Tooth Segmentation NSTL国家科技图书文献中心

Bing Wang | Chi Zhang ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 46～57 - 共12页

摘要： semi-supervised approach rooted in the foundational | In the realm of dental imaging, the | utilisation of 3D dental cone-beam computed tomography (CBCT | ) scans has gained significant prominence, particularly | in the fields of orthodontics and endodontics

关键词： Tooth segmentation | Prior knowledge | Pseudo-label | Semi-supervision

7. Multi-dimensional consistency learning between 2D Swin U-Net and 3D U-Net for intestine segmentation from CT volume NSTL国家科技图书文献中心

An, Qin | Oda, Hirohisa ... - 《International journal of computer assisted radiology and surgery.》 - 2025,20(4) - 723～733 - 共11页

摘要： network based on semi-supervised learning for intestine | semi-supervised learning and involves training both | PurposeThe paper introduces a novel two-step | segmentation from CT volumes. The intestine folds in the | abdomen with complex spatial structures and contact with

关键词： Intestine segmentation | Semi-supervision | Computer-aided diagnosis | Transformer

8. Uncertainty-assisted virtual immunohistochemical detection on morphological staining via semi-supervised learning NSTL国家科技图书文献中心

Zhou, Shun | Jin, Yanbo ... - 《Optics and Lasers in Engineering》 - 2025,184(Jan. Pt.1) - 1.1～1.9 - 共9页

摘要： this study, we present a semi-supervised learning | Tumor suppressor gene TP53 plays a crucial | role in cancer diagnosis and prognosis. The gene | encodes the tumor suppressor protein p53, which can be | identified through immunohistochemical (IHC) staining in

关键词： Bayesian uncertainty | Gastric cancer | p53 immunohistochemistry | Semi-supervision | TP53 gene | CANCER

9. Reliable semi-supervised mutual learning framework for medical image segmentation NSTL国家科技图书文献中心

Hang W. | Bai K.... - 《Biomedical signal processing and control》 - 2025,99(Jan.) - 1.1～1.11 - 共11页

摘要： this paper, we propose a reliable semi-supervised | cross pseudo supervision* (RCPS) regularization to* | supervision. Extensive experiments on both publicly | © 2024 Elsevier LtdSemi-supervised learning | (SSL) is becoming the mainstream paradigm in medical

关键词： Cognitive bias | Cross supervision | Distribution mismatch | Mutual learning | Semi-supervised learning

10. A novel FuseDecode Autoencoder for industrial visual inspection: Incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort NSTL国家科技图书文献中心

Kozamernik, Nejc | Bracun, Drago - 《Computers in Industry》 - 2025,164 - 共19页

摘要： datasets by retraining in a semi-supervised manner on | -supervision* learning on both normal and anomalous samples* | The industrial implementation of automated | visual inspection leveraging deep learning is limited | due to the labor-intensive labeling of datasets and

关键词： Automated visual inspection | Deep learning | Reconstruction-based anomaly detection | Unsupervised learning | Semi-supervised learning | Mixed supervision learning

摘要：Semi-supervised medical image segmentation | potential noise in the pseudo-supervision process | Dual-Network with Pseudo-Supervision Rectification | mechanism for single image pair and the pseudo-supervision | -supervision stage is effectively alleviated. Experimental

关键词： Semi-Supervised learning | Medical image segmentation | Competing dual-Network | Rectified pseudo-Supervision | Data augmentation

摘要： performance. In this paper, we present a cascade semi | strategies into a cycle supervision module. We evaluate C | Automatic and precise tooth segmentation is | crucial in computer aided dentistry, serving a pivotal | role in various applications likes diagnosis and

关键词： Cross pseudo supervision | Cascade network | Consistency regularization | Prior constraint

摘要： semi-supervised learning (SSL) techniques have been | propose a class-aware cross pseudo supervision (C~2PS | ) framework, which built upon cross pseudo supervision (CPS | Automatic multi-organ segmentation in | abdominal computed tomography (CT) scans is crucial for

关键词： Multi-organ segmentation | Abdominal CT | Class imbalance

摘要： propose an approach termed Cascaded Supervision | cascading supervision of the matching results at various | Local feature matching, which identifies | correspondences between image pairs, remains a fundamental | challenge in computer vision. Current methods usually

关键词： Feature matching | Cascade networks | Neighborhood consistency | Probabilistic modeling

摘要： Semi-supervised Label Propagation with Optimal Graph | Driving fatigue has become a serious hidden | danger to road traffic safety. Drivers in a fatigued | state often have problems such as delayed reactions | and lack of concentration, which increases the risk

关键词： Semi-supervision Learning | Graph Learning | Driver Fatigue Recognition | EEG data

摘要： semi-supervised approach rooted in the foundational | In the realm of dental imaging, the | utilisation of 3D dental cone-beam computed tomography (CBCT | ) scans has gained significant prominence, particularly | in the fields of orthodontics and endodontics

关键词： Tooth segmentation | Prior knowledge | Pseudo-label | Semi-supervision

摘要： network based on semi-supervised learning for intestine | semi-supervised learning and involves training both | PurposeThe paper introduces a novel two-step | segmentation from CT volumes. The intestine folds in the | abdomen with complex spatial structures and contact with

关键词： Intestine segmentation | Semi-supervision | Computer-aided diagnosis | Transformer

摘要： this study, we present a semi-supervised learning | Tumor suppressor gene TP53 plays a crucial | role in cancer diagnosis and prognosis. The gene | encodes the tumor suppressor protein p53, which can be | identified through immunohistochemical (IHC) staining in

关键词： Bayesian uncertainty | Gastric cancer | p53 immunohistochemistry | Semi-supervision | TP53 gene | CANCER

摘要： this paper, we propose a reliable semi-supervised | cross pseudo supervision (RCPS) regularization to | supervision. Extensive experiments on both publicly | © 2024 Elsevier LtdSemi-supervised learning | (SSL) is becoming the mainstream paradigm in medical

关键词： Cognitive bias | Cross supervision | Distribution mismatch | Mutual learning | Semi-supervised learning

摘要： datasets by retraining in a semi-supervised manner on | -supervision learning on both normal and anomalous samples | The industrial implementation of automated | visual inspection leveraging deep learning is limited | due to the labor-intensive labeling of datasets and

关键词： Automated visual inspection | Deep learning | Reconstruction-based anomaly detection | Unsupervised learning | Semi-supervised learning | Mixed supervision learning

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国家科技图书文献中心

摘要：Semi-supervised medical image segmentation | potential noise in the pseudo-supervision* process* | Dual-Network with Pseudo-Supervision* Rectification* | mechanism for single image pair and the pseudo-supervision | -supervision* stage is effectively alleviated. Experimental*

摘要： performance. In this paper, we present a cascade semi | strategies into a cycle supervision* module. We evaluate C* | Automatic and precise tooth segmentation is | crucial in computer aided dentistry, serving a pivotal | role in various applications likes diagnosis and

摘要： semi-supervised learning (SSL) techniques have been | propose a class-aware cross pseudo supervision* (C~2PS* | ) framework, which built upon cross pseudo supervision* (CPS* | Automatic multi-organ segmentation in | abdominal computed tomography (CT) scans is crucial for

摘要： propose an approach termed Cascaded Supervision | cascading supervision* of the matching results at various* | Local feature matching, which identifies | correspondences between image pairs, remains a fundamental | challenge in computer vision. Current methods usually

摘要： this paper, we propose a reliable semi-supervised | cross pseudo supervision* (RCPS) regularization to* | supervision. Extensive experiments on both publicly | © 2024 Elsevier LtdSemi-supervised learning | (SSL) is becoming the mainstream paradigm in medical

摘要： datasets by retraining in a semi-supervised manner on | -supervision* learning on both normal and anomalous samples* | The industrial implementation of automated | visual inspection leveraging deep learning is limited | due to the labor-intensive labeling of datasets and